Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

Abstract

In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

Towards Developing Big Data Analytics for Machining Decision-Making

“…To ensure a digital twin function well, Fattahi et al [53] suggested having humancyber-physical system-friendly BD that are easily accessible and understandable for both humans and machines. They also proposed a method for preparing the BD dataset, divided into four segments for easy integration with the digital twin's input, modeling, simulation, and validation modules.…”

Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs

“…The authors see the Cognitive DT model as one of the enablers and facilitators of the creation of the Federation of Twins. The semantic tools (such as ontologies and graph databases) bring to the DT a more human-like method [30] of processing data and information. This article empowers the need for research in the field of cognitive DT, thus contributing to the field of cognitive computing.…”